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Surgical data science for intelligent guidance and control in image-guided and robotic interventions
4 year funded PhD studentship in Surgical data science for intelligent guidance and control in image-guided and robotic interventions. Deadline for applications 31st June 2021.*NOW CLOSED*
1 June 2021
Primary Supervisor: Dr Evangelos Mazomenos (UCL MPBE, WEISS)
Susidiary Supervisor: Prof Danail Stoyanov (UCL CS, WEISS)
NOW CLOSED
A four-year funded EPSRC Doctoral Training Programme (DTP) PhD studentship is available in the UCL Department of Medical Physics and Biomedical Engineering. This position will be hosted in the Department of Medical Physics and Biomedical Engineering Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS) and involve interdisciplinary work with clinical teams. Funding will be at least the UCL minimum. Stipend details can be found here. The successful candidate will align with the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort.
Project Background
Image-guided and robotic-assisted interventions are performed with imaging devices and surgical tools inserted through natural orifices or small incisions into the anatomy, for performing therapeutic/diagnostic actions. Due to constrained space, their navigation requires dexterous manipulation and precise hand-eye coordination. These ergonomic challenges and the high-pressure nature of surgical practice add physical and cognitive load, increasing the risk for manual errors, complications, and suboptimal outcomes.
Surgical data science aims at improving the quality of interventional healthcare by leveraging information from heterogeneous data sources. These include medical imaging, electronic records, sensor devices positioned on patients, caregivers or embedded in medical instruments and the operating room. Surgical data science is a core research theme in WEISS for developing novel solutions to support decision-making and planning, intra-operative guidance, surgical training and simulation, and develop intelligent surgical robotic systems. Our objective is to translate our computational methods and technology for the operating theatre to enable less invasive and more accurate procedures which improve the safety, quality, and efficiency of interventional care.
The student will join the Surgical Robot Vision Research Group at WEISS led by Professor Danail Stoyanov and will be supervised by Dr Evangelos Mazomenos – Lecturer in Surgical Data Science in the Department of Medical Physics and Biomedical Engineering.
Research aims
The research aim of this PhD programme is to develop intelligent guidance and control methods for image-guided and robotic interventions. The candidate will employ principles from computer vision, machine learning, statistical modelling and control engineering, to analyse multimodal datasets (e.g. medical imaging, tool kinematics, wearable sensors) and model optimal surgical task execution. Outcomes will be applied for implementing intra-operative guidance, increasing autonomy in surgical robotics, improving surgical training and designing human-machine interfaces.
The candidate will benefit from collaborations with researchers within WEISS and clinical teams from the UCL network of hospitals and will have the opportunity for testing and validation of the developed solutions in simulation, ex-vivo and in-vivo settings to investigate translation in clinical practice.
Person specification & requirements:
Candidates must have a UK (or international equivalent) first class or 2:1 honours degree preferably in computer science, mathematics, engineering, or a comparable subject.
The ideal applicant will have an MSc in data science, computer vision or automatic control. The student is expected to have the desire to work in an interdisciplinary environment and keen interest in biomedical engineering research that has a positive impact on the delivery of interventional healthcare.
Person specification & requirements
Candidates must have a UK (or international equivalent) first class or 2:1 honours degree preferably in computer science, mathematics, engineering, or a comparable subject.
The ideal applicant will have an MSc in data science, computer vision or automatic control. The student is expected to have the desire to work in an interdisciplinary environment and keen interest in biomedical engineering research that has a positive impact on the delivery of interventional healthcare.
Good level of mathematical and computing skills and solid experience in computer programming (e.g. Python, MATLAB or similar) for data processing and algorithm development are essential. The student is also expected to demonstrate creative and critical thinking; excellent writing and oral communication skills; good working habits; ability for taking initiatives and working both in an independent and collaborative environment.
Experience with any of data modelling and analysis, computer vision, machine learning or control engineering, particularly with prior exposure in complex medical datasets would be advantageous but not essential.
How to apply
Please send an expression of interest as a brief cover letter, current CV and names of two referees to Dr Evangelos Mazomenos (e.mazomenos@ucl.ac.uk) and cdtadmin@ucl.ac.uk. Please quote Project code: 21004 in the subject title.
Make a formal application to via the UCL application portal https://www.ucl.ac.uk/prospective-students/graduate/apply . Please select the programme code Medical Imaging TMRMEISING01 and enter Project Code 21004 under ‘Name of Award 1’
For project-specific queries and information about the position please contact Dr Evangelos Mazomenos (e.mazomenos@ucl.ac.uk).
Application Deadline *NOW CLOSED TO APPLICATIONS*
The closing date is 31st June 2021 and the position is anticipated to start in September 2021. Applications will be assessed on a rolling basis so please do apply as early as possible.
Funding is available for 4 years, covering university fees and a tax free stipend (details here).
For funding details please consult the following webpage